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Effective AI-agent observability follows the entire execution graph, not just the incoming request and final answer. Capture a trace for each consequential workflow, add metrics for reliability, latency, resource use and cost, evaluate output quality and safety continuously, and create alerts only when an operator has a clear next action.
A useful operating model is simple: trace every important step, measure every production risk, evaluate whether the result met the workflow contract, and preserve enough structured context to explain failures without exposing sensitive prompts or data.
Why a flat request log misses agent failures
A conventional web request can often be represented by one start event, one status code and one duration. An agent run is a graph of model calls, retrieval, tool execution, retries, handoffs, approvals and post-processing. The final HTTP response can be successful even when a tool returned stale data, a retrieval step found nothing, a policy check denied an action, or the agent quietly exceeded its cost budget.
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Build one trace around every consequential workflow
Create one root trace for each user request, scheduled job or business transaction. Give the trace a workflow ID and retain the deployment, prompt and agent versions needed to interpret it later. Child spans should explain every operation that can affect the result, cost, safety or user experience.
| Span or event | Capture | Why it matters |
|---|---|---|
| Agent start and finish | Agent name and version, environment, tenant or workflow ID, start and end timestamps, final outcome | Connects a user-visible result to the exact build and execution context |
| Model call | Provider, model, request and response times, finish reason, input and output token counts, and streaming time to first token when available | Explains latency, truncation, token growth, model changes and cost |
| Tool call | Tool name and version, sanitized arguments, authorization or policy decision, duration, result status, retry count, timeout and normalized error type | Shows whether the agent failed while acting rather than while generating text |
| Retrieval | Query identifier, data source, document identifiers, relevance scores and reranker details when used | Distinguishes a reasoning problem from missing, irrelevant or poorly ranked evidence |
| Handoff or planning step | Source and destination agents, reason, plan step, approval state and timestamps | Reveals loops, stalled handoffs and unauthorized delegation |
| Final outcome | Business result, escalation or fallback decision, evaluator labels and user-visible status | Lets success be measured by completed work rather than an HTTP 200 response |
OpenTelemetry’s GenAI examples use an invoke_agent span with child chat and execute_tool spans. Their attributes cover model identity, token usage, finish reasons, agent identity, conversation identity, provider, retrieval documents and scores, with message content treated as optional. Keep span names and attributes stable; put changing identifiers in attributes rather than creating a new event name for every value.
Protect prompts, completions and tool payloads
Full prompts, completions, tool arguments and retrieved documents can contain personal, confidential or security-sensitive information. Do not record unrestricted content in every environment by default. Use explicit opt-in capture, redaction and truncation, role-based access, sampling, short retention periods and a separate encrypted store for payloads that investigators may access under authorization. Keep trace metadata useful even when content is removed: record identifiers, hashes where appropriate, normalized error classes, policy outcomes and links to the secure payload record.
Metrics that deserve dashboard space
Choose metrics that map to a workflow contract. A dashboard with dozens of counters but no connection to user outcomes encourages local optimization: reducing latency by skipping retrieval, for example, can lower answer quality. Track the following groups together.
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Reliability and workflow health
- Request volume and completed-workflow volume.
- Success rate based on the business outcome, such as an accurately resolved case or safely completed operation, not merely transport status.
- Separate error rates for model calls, tools, retrieval and handoffs.
- Timeout, cancellation and fallback rates.
- Retries per run and the rate of loops or runaway steps.
- Human-escalation rate, including the reason for escalation.
Keep denominator definitions explicit. A tool error rate should count tool attempts, while a workflow failure rate should count workflows. Mixing those denominators can make a healthy component appear broken or hide a component that fails only once but causes many workflow failures.
Latency and streaming experience
Measure end-to-end latency and break it into queueing, model, retrieval, tool, handoff and post-processing time. Use p50 for the typical experience and p95 or p99 for tail behavior on user-facing paths. Streaming systems need two separate measures: time to first token, which reflects perceived responsiveness, and time to final answer, which reflects completion time. OpenTelemetry documents the gen_ai.client.operation.duration metric and model-level filtering for latency analysis.
Tokens, cost and resource use
Count input and output tokens by model, workflow, tenant and version. Estimate cost from the price table maintained by your organization rather than hard-coding a rate into telemetry. Add tokens per successful task and cost per successful task; these expose prompt growth, excessive retries and expensive workflows that have a deceptively low per-call cost. Also watch queue depth, concurrency, rate-limit responses and provider capacity when those affect completion.
Quality and safety
Use offline and online evaluations for task success, correctness or faithfulness, relevance, groundedness, toxicity or safety, policy compliance and tool-call accuracy. Combine automated judges with sampled human review and a versioned evaluation set. A single quality score cannot represent every agent: a support workflow may require resolution and escalation accuracy, a research workflow may require citation correctness and completeness, and an operations workflow may require safe execution and reliable rollback.
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Use structured events and logs for searchable detail
Metrics answer how often and how much; traces answer what happened in one execution; events and logs answer which named condition occurred and provide searchable context. OpenTelemetry’s event guidance treats an event as a named occurrence or state change, while a span represents an operation with duration.
Stable event names
Useful event names include tool.validation_failed, tool.authorization_denied, retrieval.empty, model.rate_limited, agent.handoff, human.approval_requested, policy.violation, evaluation.completed and workflow.completed. Include the trace ID, span ID, workflow ID, agent and version, severity, normalized error type and a redacted reason. Keep the event name stable and place variable IDs, tenant IDs and counts in attributes.
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Record model and tool identifiers, normalized error class, retry decision, latency bucket, policy result and evaluator labels. If an investigator needs the original payload, store a reference to the protected payload system and enforce authorization there. This approach supports replay and diagnosis while limiting the blast radius of a telemetry breach.
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Alerts that an operator can act on
Alert on sustained, user-relevant conditions rather than every unusual trace. Set thresholds from the workflow’s error budget, latency objective, cost budget and quality baseline; there is no universal percentage that applies to every agent.
- Error or timeout budget breach: trigger when the sustained rate exceeds the workflow’s defined budget. Link to traces grouped by model, tool and error class.
- Tail-latency regression: alert on p95 or p99 end-to-end latency and show the component breakdown so an operator can distinguish queueing from a slow provider or tool.
- Cost increase: alert when tokens or estimated cost per successful task exceed the approved budget, not merely when raw traffic rises.
- Tool and authorization anomalies: detect spikes in tool failures, denied actions, timeouts or repeated retries. Include the affected tool version and policy rule.
- Quality-floor breach: alert when an online evaluation score falls below the workflow’s floor or drops sharply against its versioned baseline. Attach representative failing examples.
- Safety or privacy incident: page immediately for policy violations, unsafe actions or suspected sensitive-data exposure and provide containment instructions.
- Unexpected change: detect a model, provider, prompt, agent-version or traffic shift that was not part of the deployment plan.
Every alert should identify an owner and a next action: roll back a deployment, disable a tool, switch provider, raise capacity, update a prompt, tighten a policy or add a regression test. Include links to representative traces, the deployment or prompt version and the evaluation examples that caused the condition. An alert that only says a score moved creates noise rather than operational control.
A practical rollout sequence
- Select one consequential workflow. Write its user-visible success condition and define what counts as a safe failure, fallback or escalation.
- Instrument the execution graph. Add the root request span and child spans for model calls, tools, retrieval, retries, handoffs, approvals and final outcome using OpenTelemetry-compatible attributes.
- Establish a baseline. Measure latency percentiles, token use, estimated cost, errors, retries, escalation and successful-task rate for each model, workflow and version.
- Create a small evaluation set. Start with known-good examples and real production failures. Version the data and label the dimensions that matter to the workflow.
- Add a few high-value alerts. Tie them to error budgets, latency, cost, quality and safety. Route each to an owner with a documented runbook.
- Review traces weekly. Sample representative successes and failures. Turn recurring failure modes into evaluation cases, structured events and code, tool or prompt fixes.
This creates an improvement loop rather than a one-time dashboard. Phoenix describes a workflow combining tracing, evaluation, datasets, experiments and production monitoring; Datadog combines tracing, testing, evaluation and monitoring in one commercial platform.
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Compare platforms on the same operational axes instead of choosing by dashboard appearance. Ask how much instrumentation is automatic, how deeply traces cover tools and retrieval, whether evaluators can run offline and online, how incidents connect to traces, and how data governance works in your environment.
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| Option | Strengths described by its documentation | Best fit and questions to verify |
|---|---|---|
| Arize Phoenix | Open-source observability for experimentation, evaluation and troubleshooting; OpenTelemetry/OpenInference instrumentation; LLM and tool tracing; retrieval inspection; token, latency, cost and error analysis; evaluators, datasets, experiments and human feedback | Teams wanting an open-source workflow. Verify deployment, retention, access control and the storage backend you require. |
| Datadog Agent Observability | Commercial platform combining end-to-end LLM tracing, datasets, experiments, testing, offline and online evaluations, prompt workflows, human review and production monitoring | Organizations already operating Datadog. Verify ingestion, evaluator-model costs, data residency and retention terms for your account. |
| LangSmith | Thread-level evaluation and the ability to feed production traces into evaluation and improvement workflows | Applications built around LangChain or LangGraph. Verify support for non-framework components and export portability. |
| OpenTelemetry-first stack | Portable instrumentation and semantic attributes across collectors and backends; conventions cover traces, metrics, logs, events and resources | Teams prioritizing backend choice and governance. Expect to assemble evaluation, alerting and incident workflows and monitor evolving GenAI conventions. |
For any option, check language SDK coverage, automatic versus manual spans, replay and search quality, custom evaluators, human review, SLO integration, cardinality controls, sampling, self-hosting, data residency, redaction, access control, export and portability. Also account for ingestion and retention pricing, evaluator-model usage and the engineering effort required to maintain instrumentation.
Diagnose common symptoms from the trace outward
The final answer is wrong but latency is normal
Inspect retrieval spans and document scores first, then tool results and policy decisions. An empty or low-relevance retrieval event points to indexing, query construction or reranking; a correct retrieval followed by a wrong answer points toward model behavior or prompt logic. Add the failing example to the evaluation set after fixing the cause.
Latency suddenly increases
Compare p95 component spans with the previous deployment. A longer queue indicates capacity pressure; longer model spans suggest provider, model or token changes; longer tools or retrieval indicate downstream dependencies. Time to first token and time to final answer reveal whether the problem is perceived responsiveness or total completion time.
Costs rise while traffic is flat
Break tokens down by workflow, model and version, then inspect retries and loop counts. Prompt growth, larger retrieved context, a model fallback or repeated tool attempts can raise cost without increasing requests. Alert on cost per successful task so a rising failure rate cannot make the metric appear healthy.
Users report duplicate or unsafe actions
Follow tool spans, authorization decisions, retries and handoffs in one root trace. Look for non-idempotent tools retried after an ambiguous timeout, policy checks that occurred after execution, or planner loops. Contain the affected tool, require human approval where appropriate, and add a regression test for the exact sequence.
What good observability looks like
An operator investigating one failed workflow should be able to open its root trace, see every model, retrieval, tool and handoff step, identify the deployment and policy versions, understand retry decisions, inspect evaluator labels and locate protected payloads when authorized. A manager reviewing a week should be able to compare successful-task rate, tail latency, token and cost efficiency, quality and safety across workflows without mistaking traffic volume for performance.
That combination—portable semantic conventions, sensitive-data controls, outcome-based metrics, versioned evaluations and action-oriented alerts—turns agent monitoring from a collection of logs into a system for preventing user-visible failures.
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